Load Data and Environment: Topic introduction

In this part of the course, we will cover the following concepts:

• Load data into R
• Manipulate variables within an R environment

Warm-up

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File Paths

File Paths

Setting your path

• Instead of moving to and from geographic locations, locating a file on your computer involves moving from your home directory (or folder) to deeper, nested directories

• When writing path names,

  • begin with the home directory, which is often the username for the computer
  • separate each directory with a backslash (/) and no spaces
  • make sure it ends with the name of the desired file with the proper file extension
  • and encapsulate it within single or double quotations marks

Setting your path (contd)

• How would you write out the path to the CSV file?
• We would give R the following directions to the CSV
  • "/Users/datasociety/Documents/Data/data.csv" for Mac
  • "C:/Users/datasociety/Documents/Data/data.csv" for Windows

R’s working directory

Setting the working directory via R

# Check the current working directory. 
# Note: your directory path may look different.
getwd()

[1] "/opt/atlassian/pipelines/agent/build/slides/IntermediateR/LoadDataAndEnvironment"

# Set the working directory. 
# Note: your directory path may look different.
setwd()

Setting the working directory via GUI

R’s default working directory

Directory settings

• We can encode directory paths into variables and then change them without having to manually type the paths every time

• In order to maximize the efficiency of your workflow, you may want to use the box package and encode your directory structure into variables

install.packages("box")

• Let the main_dir be the variable corresponding to your materials folder
• Use dirname() to navigate through your computer’s file hierarchy

# Set `main_dir` to the location of your materials folder.
# Note: your directory path may look different.

path = box::file()
main_dir = dirname(dirname(path))

Directory settings (cont’d)

# Make `data_dir` from the `main_dir` and
# remainder of the path to data directory.
data_dir = paste0(main_dir, "/data")
# Make `plots_dir` from the `main_dir` and
# remainder of the path to plots directory.
plot_dir = paste0(main_dir, "/plots")

list.files(path = data_dir, pattern="temp")

[1] "temp_heart_rate_subset.csv" "temp_heart_rate.csv"       

Loading dataset into R: read CSV files

• Most of the time, we work with data generated elsewhere that we need to load into our R environment
• R works with many different data types, but the most common one is CSV

# To read a C[omma] S[eparated] V[alues] file into
# R you can use a simple command `read.csv`.
temp_heart_data = read.csv(file = file.path(data_dir, "temp_heart_rate.csv"),  #<- provide file path
                           header = TRUE,            #<- if file has header set to TRUE
                           stringsAsFactors = FALSE) #<- read strings as characters, not as factors

• In R, factors are variables with a limited number of different values, (i.e., categorical variables)
• Since we haven’t profiled this dataset to know whether all strings should be converted to factors, we will leave stringsAsFactors set to FALSE

Viewing data in R

• First, we can take a general look into our dataset structure with str()

# Inspect the structure of the data.
str(temp_heart_data)

'data.frame':   130 obs. of  3 variables:
 $ Gender    : chr  "Male" "Male" "Male" "Male" ...
 $ Body.Temp : num  96.3 96.7 96.9 97 97.1 97.1 97.1 97.2 97.3 97.4 ...
 $ Heart.Rate: int  70 71 74 80 73 75 82 64 69 70 ...

• What other R functions can we use to explore our data?

Viewing data in R (cont’d)

• Then, we can inspect the head or tail of our data with head() or tail() function
• By default, head() will give you the first six rows and tail() will give you the last six
• However, you can also adjust the number of rows as the following example illustrates

head(temp_heart_data, 4) #<- Inspect the `head` (first 4 rows).

  Gender Body.Temp Heart.Rate
1   Male      96.3         70
2   Male      96.7         71
3   Male      96.9         74
4   Male      97.0         80

tail(temp_heart_data, 4) #<- Inspect the `tail` (last 4 rows).

    Gender Body.Temp Heart.Rate
127 Female      99.4         77
128 Female      99.9         79
129 Female     100.0         78
130 Female     100.8         77

Viewing data in R (cont’d)

View(temp_heart_data)

Other file types and commands in R

• Reading in other files is a matter of specifying their type

Saving data: write CSV files

• The most common way to share tabular data is by saving your data to a CSV file

# Let's save the first 10 rows of our data to a variable.
temp_heart_subset = temp_heart_data[1:10, ]
temp_heart_subset

   Gender Body.Temp Heart.Rate
1    Male      96.3         70
2    Male      96.7         71
3    Male      96.9         74
4    Male      97.0         80
5    Male      97.1         73
6    Male      97.1         75
7    Male      97.1         82
8    Male      97.2         64
9    Male      97.3         69
10   Male      97.4         70

# Write data to a CSV file providing 3 arguments:
write.csv(temp_heart_subset,            #<- name of variable to save 
          file.path(data_dir, "temp_heart_rate_subset.csv"), #<- name of file where to save
          row.names = FALSE)            #<- logical value for row names

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Clearing objects from environment

• Suppose we conclude a coding session and want to remove objects from the environment, or free up a variable name for reassignment

# List all objects in environment.
ls()

 [1] "conda_yaml"        "data_dir"          "env_name"         
 [4] "main_dir"          "params"            "platform"         
 [7] "plot_dir"          "session_info"      "temp_heart_data"  
[10] "temp_heart_subset"

# Remove individual variable(s).
rm(X, x, this_is_a_valid_name, This.Is.Also.A.Valid.Name, unnamed_list) #<- example
rm(list=ls()) #<- actual command

# List all objects again to check.
ls()

character(0)

• Notice the variables we have removed are gone

Clearing the entire environment

• A clear environment will always appear like this in the Environment pane

• You can also clear the environment by clicking on the broom icon at the top of the Environment pane

Module completion checklist

Knowledge check

Exercise

Module completion checklist

Load Data and Environment: Topic summary

In this part of the course, we have covered:

• Load data into R
• Manipulate variables within R environment

Congratulations on completing this module!